{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a132fec0-19a4-490b-b856-9ff6208f4d7d",
   "metadata": {},
   "source": [
    "# Pinocchio + CasADi: variational integrators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0217549-d6bf-4838-a2eb-befef70bcc59",
   "metadata": {},
   "source": [
    "## Variational integrators: introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f861c3ba-c1ce-40bd-80a2-1d6fb18f5a67",
   "metadata": {},
   "source": [
    "$$\n",
    "\\newcommand{\\bfm}[1]{\\mathbf{#1}}\n",
    "$$\n",
    "\n",
    "We're going to implement an integrator following [Murphey's 2009 paper](https://murpheylab.github.io/pdfs/2009TROJoMuextended.pdf).\n",
    "\n",
    "The idea is as follows: the continuous Euler-Lagrange equation underpinning classical analytical mechanics can be used to derive the Newton-Euler equation in generalized coordinates,\n",
    "$$\n",
    "    M(\\bfm{q})\\bfm{\\ddot q} + h(\\bfm q, \\bfm{\\dot q}) = \\boldsymbol{\\tau}\n",
    "$$\n",
    "These equations are often discretized using methods for DAEs or ODEs, by introducing the acceleration\n",
    "$$\n",
    "    \\bfm{a} = M(\\bfm{q})^{-1}(\\boldsymbol\\tau - h(\\bfm{q}, \\bfm{\\dot{q}})) \\triangleq \\mathrm{ABA}(\\bfm q, \\bfm{\\dot{q}}, \\boldsymbol\\tau)\n",
    "$$\n",
    "where ABA standard for \"articulated body algorithm\", and using standard integrators like semi-implicit Euler, Runge-Kutta..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d4cef6-183d-4113-951b-c607b6f6af14",
   "metadata": {},
   "source": [
    "The Euler-Lagrange equation itself (with external forces) reads\n",
    "$$\n",
    "    \\frac{d}{dt}\\frac{\\partial \\mathcal L}{\\partial \\bfm{\\dot q}} = \\frac{\\partial\\mathcal L}{\\partial\\bfm{q}} + \\boldsymbol{\\tau}\n",
    "$$\n",
    "where $ \\mathcal L(\\bfm q, \\bfm{\\dot q}) = T(\\bfm{q}, \\bfm{\\dot q}) - V(\\bfm{q}) $ is the dynamical system's Lagrangian. It is derived from the Lagrange-d'Alembert principle\n",
    "\\begin{equation}\n",
    "    \\delta S + \\int_{t_0}^{t_f} \\boldsymbol{\\tau} \\cdot \\delta \\bfm{q}\\, dt = 0\n",
    "    \\tag{P}\n",
    "\\end{equation}\n",
    "where $ S(\\bfm q) = \\int_{t_0}^{t_f} L(\\bfm q, \\bfm{\\dot q})\\,dt $ is the action integral."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e09cee-4c6c-442c-960c-0908165b0bae",
   "metadata": {},
   "source": [
    "**_Variational integrators_** rely on a direct discretization of principle (P) instead of discretizing the Euler-Lagrange equation itself.\n",
    "The resulting condition is the discrete Euler-Lagrange (DEL) equation\n",
    "$$\n",
    "    \\partial_2L_d(\\bfm{q}_{k-1}, \\bfm{q}_{k}) + \\partial_1L_d(\\bfm{q}_k, \\bfm{q}_{k+1}) + \\Delta t\\boldsymbol{\\tau}_k = 0\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a600f149-1967-4319-a395-5946eb467468",
   "metadata": {},
   "source": [
    "The function $L_d$ is the _discrete Lagrangian_, which is a quadrature term for the action, for instance using the midpoint rule:\n",
    "$$\n",
    "    L_d(\\bfm{q}_k, \\bfm{q}_{k+1}) = L(\\bfm{\\bar{q}}, \\tilde{\\bfm{v}})\\Delta t,\\quad\n",
    "    \\bfm{\\bar{q}} = \\mathrm{interpolate}(\\bfm q_k, \\bfm{q}_{k+1}, 1/2), \\\n",
    "    \\tilde{\\bfm{v}} = \\frac{\\bfm{q}_{k+1} \\ominus \\bfm{q}_k}{\\Delta t}.\n",
    "$$\n",
    "(Alternatives are possible for instance using the trapezoidal rule). We have that\n",
    "$$\n",
    "    S(\\bfm q) \\approx \\sum_{k=0}^{N-1} L_d(\\bfm{q}_k, \\bfm{q}_{k+1}).\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f230c94-bb6b-4198-8d94-6b0c4e17f3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import casadi\n",
    "import hppfcl as fcl\n",
    "import numpy as np\n",
    "import pinocchio as pin\n",
    "import pinocchio.casadi as cpin\n",
    "from casadi import SX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "255320ce-b012-4e70-a61c-fadc0efa169d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeModel():\n",
    "    model = pin.Model()\n",
    "    geom_model = pin.GeometryModel()\n",
    "\n",
    "    RED_COLOR = np.array([1.0, 0.1, 0.1, 1.0])\n",
    "    WHITE_COLOR = np.ones(4)\n",
    "\n",
    "    joint_pl = pin.SE3.Identity()\n",
    "    mass = 1.0\n",
    "    radius_base = 0.08\n",
    "    shape0 = fcl.Sphere(radius_base)\n",
    "    geom_obj = pin.GeometryObject(\"base\", 0, shape0, pin.SE3.Identity())\n",
    "    geom_obj.meshColor = RED_COLOR\n",
    "    geom_model.addGeometryObject(geom_obj)\n",
    "\n",
    "    parent_id = 0\n",
    "\n",
    "    for k in range(2):\n",
    "        # define joint and body\n",
    "        j_name = f\"joint{k}\"\n",
    "        j_id = model.addJoint(parent_id, pin.JointModelRY(), joint_pl, j_name)\n",
    "\n",
    "        radius = 0.06\n",
    "        mass = 0.5\n",
    "        length = 1.0\n",
    "\n",
    "        body_pl = joint_pl.copy()\n",
    "        body_pl.translation[2] = length  # at end of joint\n",
    "        inertia = pin.Inertia.FromSphere(mass, radius)\n",
    "        model.appendBodyToJoint(j_id, inertia, body_pl)\n",
    "\n",
    "        # add geometry\n",
    "        geom_name = f\"ball{k}\"\n",
    "        shape = fcl.Sphere(radius)\n",
    "        geom_obj = pin.GeometryObject(geom_name, j_id, shape, body_pl)\n",
    "        geom_obj.meshColor = RED_COLOR\n",
    "        geom_model.addGeometryObject(geom_obj)\n",
    "\n",
    "        geom_name2 = f\"capsule{k}\"\n",
    "        geom_pl2 = body_pl.copy()\n",
    "        geom_pl2.translation = np.array([0.0, 0.0, length / 2])\n",
    "        shape2 = fcl.Cylinder(radius / 3, length)\n",
    "        geom_obj2 = pin.GeometryObject(geom_name2, j_id, shape2, geom_pl2)\n",
    "        geom_obj2.meshColor = WHITE_COLOR\n",
    "        geom_model.addGeometryObject(geom_obj2)\n",
    "\n",
    "        parent_id = j_id\n",
    "        joint_pl = body_pl.copy()\n",
    "\n",
    "    return model, geom_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d292e572-b5bd-4984-b680-b09861864c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nb joints = 3 (nq=2,nv=2)\n",
       "  Joint 0 universe: parent=0\n",
       "  Joint 1 joint0: parent=0\n",
       "  Joint 2 joint1: parent=1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model, geom_model = makeModel()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90a8319-4773-4a2b-946b-d94b205ccf1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pinocchio.visualize import MeshcatVisualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61ede1ad-58c8-417c-b83f-8b452cc2eef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vizer = MeshcatVisualizer(model, geom_model, visual_model=geom_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77fcacdf-2715-4cb6-bb6c-c2ea62c48b86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can open the visualizer by visiting the following URL:\n",
      "http://127.0.0.1:7000/static/\n"
     ]
    }
   ],
   "source": [
    "vizer.initViewer()\n",
    "vizer.loadViewerModel(\"pinocchio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfb5d8b8-5c22-4ebb-838b-62ab297c743b",
   "metadata": {},
   "outputs": [],
   "source": [
    "q0 = pin.neutral(model)\n",
    "vizer.display(q0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "726605b6-2cc1-4599-9ff5-2cb8d233c0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div style=\"height: 400px; width: 100%; overflow-x: auto; overflow-y: hidden; resize: both\">\n",
       "            <iframe src=\"http://127.0.0.1:7000/static/\" style=\"width: 100%; height: 100%; border: none\"></iframe>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vizer.viewer.jupyter_cell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3fef46ef-3ca5-4d7d-8ef4-0140d4fcfa48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the timestep\n",
    "timestep = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ec9808c-1e64-4fcd-a4f4-369229d54581",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the system Lagrangian's graph\n",
    "\n",
    "\n",
    "def continuousLagrangian(model, data, q, v, ns=cpin):\n",
    "    return ns.computeKineticEnergy(model, data, q, v) - ns.computePotentialEnergy(\n",
    "        model, data, q\n",
    "    )\n",
    "\n",
    "\n",
    "def discreteLagrangian(model, data, q0, q1, alpha=0.5, ns=cpin):\n",
    "    q = ns.interpolate(model, q0, q1, alpha)\n",
    "    v = ns.difference(model, q0, q1) / timestep\n",
    "    return continuousLagrangian(model, data, q, v, ns=ns) * timestep\n",
    "\n",
    "\n",
    "## define the symbols\n",
    "cmodel = cpin.Model(model)\n",
    "cdata = cmodel.createData()\n",
    "\n",
    "cq0 = SX.sym(\"q0\", model.nq)\n",
    "cq1 = SX.sym(\"q1\", model.nq)\n",
    "cq2 = SX.sym(\"q2\", model.nq)\n",
    "cdq0_ = SX.sym(\"dq0_\", model.nv)\n",
    "cdq1_ = SX.sym(\"dq1_\", model.nv)\n",
    "cdq2_ = SX.sym(\"dq2_\", model.nv)\n",
    "\n",
    "# control symbol\n",
    "nu = model.nv  # assume fully-actuated\n",
    "cu = SX.sym(\"u\", nu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5246df7b-0392-415e-a515-6a5118d66baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Function(f:(q0[2],q1[2],q2[2],u[2])->(f[2]) SXFunction)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_residual_expr():\n",
    "    cq0_int = cpin.integrate(cmodel, cq0, cdq0_)\n",
    "    cq1_int = cpin.integrate(cmodel, cq1, cdq1_)\n",
    "    cq2_int = cpin.integrate(cmodel, cq2, cdq2_)\n",
    "\n",
    "    Ld_curr = discreteLagrangian(cmodel, cdata, cq0_int, cq1_int)\n",
    "    Ld_next = discreteLagrangian(cmodel, cdata, cq1_int, cq2_int)\n",
    "    D2Ld_curr = casadi.gradient(Ld_curr, cdq1_)\n",
    "    D1Ld_next = casadi.gradient(Ld_next, cdq1_)\n",
    "    Fd = timestep * cu  # rectangle rule\n",
    "\n",
    "    residual = D2Ld_curr + D1Ld_next + Fd\n",
    "    return residual\n",
    "\n",
    "\n",
    "residual = create_residual_expr()\n",
    "nv = model.nv\n",
    "cargs = [cq0, cq1, cq2, cu, cdq0_, cdq1_, cdq2_]\n",
    "fused_dq = casadi.vertcat(cdq0_, cdq1_, cdq2_)\n",
    "carg_names = [\"q0\", \"q1\", \"q2\", \"u\", \"dq0_\", \"dq1_\", \"dq2_\"]\n",
    "\n",
    "residual_fun = casadi.Function(\n",
    "    \"f\",\n",
    "    cargs[:4],\n",
    "    [casadi.substitute(residual, fused_dq, SX.zeros(nv + nv + nv))],\n",
    "    carg_names[:4],\n",
    "    [\"f\"],\n",
    ")\n",
    "residual_fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17110958-2fe6-405a-bed6-a879ac02df06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DM([0.01, 0.01])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residual_fun(pin.neutral(model), pin.neutral(model), pin.neutral(model), np.ones(nu))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "66bccb72-1eb3-42f4-823e-2645d3bbf23b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_jacobian_fun_all():\n",
    "    # define a function to compute the Jacobians of the residual\n",
    "    # wrt variables q0, q1, q2 and u\n",
    "    fused_args = casadi.vertcat(fused_dq, cu)\n",
    "\n",
    "    jac_residual_expr_full = casadi.jacobian(residual, fused_args)\n",
    "    nv = model.nv\n",
    "    jac_residual_blocks = [\n",
    "        jac_residual_expr_full[:, :nv],\n",
    "        jac_residual_expr_full[:, nv : 2 * nv],\n",
    "        jac_residual_expr_full[:, 2 * nv : 3 * nv],\n",
    "        jac_residual_expr_full[:, 3 * nv :],\n",
    "    ]\n",
    "    jac_residual_blocks = [\n",
    "        casadi.substitute(J, fused_dq, SX.zeros(nv + nv + nv))\n",
    "        for J in jac_residual_blocks\n",
    "    ]\n",
    "    assert jac_residual_blocks[-1].shape == (nu, nv)\n",
    "    jac_residual_fun = casadi.Function(\n",
    "        \"Df\", cargs[:4], jac_residual_blocks, carg_names[:4], [\"f0\", \"f1\", \"f2\", \"fu\"]\n",
    "    )\n",
    "    return jac_residual_fun\n",
    "\n",
    "\n",
    "jac_residual_fun = make_jacobian_fun_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efb0f415-8aeb-4f01-ac7f-4bd24a97dfe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Function(Df:(q0[2],q1[2],q2[2],u[2])->(f0[2x2],f1[2x2],f2[2x2],fu[2x2,2nz]) SXFunction)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jac_residual_fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e66b07fc-1012-4a7b-af0b-7911ba8cbba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm.auto as tqdm\n",
    "\n",
    "\n",
    "def mechanicalEnergyFromQs(model, data, q0, q1, alpha=0.5):\n",
    "    q = pin.interpolate(model, q0, q1, alpha)\n",
    "    v = pin.difference(model, q0, q1) / timestep\n",
    "    return pin.computeMechanicalEnergy(model, data, q, v)\n",
    "\n",
    "\n",
    "pdata = model.createData()\n",
    "\n",
    "\n",
    "def normInf(v):\n",
    "    return np.linalg.norm(v, ord=np.inf)\n",
    "\n",
    "\n",
    "def variational_integrator(q0, q1, u, Tf):\n",
    "    states_ = [q0, q1]\n",
    "    nsteps = int(Tf / timestep)\n",
    "\n",
    "    Em_vals = [mechanicalEnergyFromQs(model, pdata, q0, q1)]\n",
    "    TOL = 1e-8\n",
    "\n",
    "    tnr = tqdm.trange(1, nsteps)\n",
    "    for t in tnr:\n",
    "        qp = states_[t - 1].copy()\n",
    "        qc = states_[t].copy()\n",
    "\n",
    "        # newton procedure\n",
    "        # semi-explicit warm start\n",
    "        vc = pin.difference(model, qp, qc) / timestep\n",
    "        ac = pin.aba(model, pdata, qc, vc, u)\n",
    "        vn_guess = vc + ac * timestep\n",
    "        qn_guess = pin.integrate(model, qc, vn_guess * timestep)\n",
    "\n",
    "        max_iters = 20\n",
    "        for _ in range(max_iters):\n",
    "            fval = residual_fun(qp, qc, qn_guess, u).full()\n",
    "            res_norm = normInf(fval)\n",
    "            if res_norm < TOL:\n",
    "                break\n",
    "            Jf2 = jac_residual_fun(qp, qc, qn_guess, u)[2].full()  # derivative in qnext\n",
    "            z = -np.linalg.solve(Jf2, fval)\n",
    "\n",
    "            # adaptive Newton\n",
    "            atau = 0.4\n",
    "            alpha = min(np.sqrt(2 * atau / res_norm), 1.0)\n",
    "            qn_guess[:] = pin.integrate(model, qn_guess, alpha * z)\n",
    "\n",
    "        conv = abs(max(fval)) < TOL\n",
    "        if not conv:\n",
    "            print(\"Did not converge.\")\n",
    "\n",
    "        qn = qn_guess\n",
    "        states_.append(qn)\n",
    "        e = mechanicalEnergyFromQs(model, pdata, qc, qn)\n",
    "        Em_vals.append(e)\n",
    "    return states_, Em_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cc42e6-8d8b-4421-8e35-c9a8fc0f713f",
   "metadata": {},
   "source": [
    "We define an initial $\\bfm{q}_0$ from a disturbed neutral position, and $\\bfm{q}_1 = \\bfm{q}_0 \\oplus \\Delta t \\bfm{v}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39aada47-f29b-49ac-95ee-6fccf4f09a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "disturbance_ = np.random.randn(nv) * 0.02\n",
    "q0 = pin.integrate(model, pin.neutral(model), disturbance_)\n",
    "v0 = np.zeros(nv)\n",
    "u0 = np.zeros(nv)\n",
    "q1 = pin.integrate(model, q0, v0 * timestep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28719d6c-6c76-473c-9b9d-f3a74ef9462a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df4f504f28204bdbaf83e653e58777ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/499 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tf = 5  # in seconds\n",
    "states_, Em_vals = variational_integrator(q0, q1, u=u0, Tf=Tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1236dece-c2dd-4181-899c-f6376af67e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(501, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_ = np.stack(states_)\n",
    "states_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a0c3f51-69b9-4df3-af35-994f97dd61f1",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_traj = states_.T\n",
    "vizer.display(q0)\n",
    "vizer.viewer.jupyter_cell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98b71639-ea49-489f-9c88-e6ed788d0513",
   "metadata": {},
   "outputs": [],
   "source": [
    "vizer.play(q_traj, timestep)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5e645f-2a7b-46b0-9093-3597f568ad49",
   "metadata": {},
   "source": [
    "Plot the mechanical energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "861bedee-611d-4aac-b781-62d4edaef6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "seaborn.set_style()\n",
    "\n",
    "times = np.linspace(0.0, Tf, int(Tf / timestep))\n",
    "plt.plot(times, Em_vals, lw=1.0, ls=\"--\")\n",
    "plt.title(\"Mechanical energy\")\n",
    "plt.xlabel(\"Time $t$\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfb487f-49b8-4ee3-86bb-82b36c3c432a",
   "metadata": {},
   "source": [
    "## Comparison with semi-implicit Euler integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c8f9d96-36a4-4b68-8e0b-6af1a274cbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def semi_implicit_integrator(q0, v0, u, Tf):\n",
    "    nsteps = int(Tf / timestep)\n",
    "    x0 = np.concatenate([q0, v0])\n",
    "    states_ = [x0]\n",
    "    Em_vals = []\n",
    "    for t in range(nsteps):\n",
    "        xc = states_[t]\n",
    "        qc, vc = xc[: model.nq], xc[model.nq :]\n",
    "\n",
    "        ac = pin.aba(model, pdata, qc, vc, tau=u)\n",
    "        vn = vc + ac * timestep\n",
    "        qn = pin.integrate(model, qc, vn * timestep)\n",
    "        xn = np.concatenate([qn, vn])\n",
    "\n",
    "        states_.append(xn)\n",
    "        e = pin.computeMechanicalEnergy(model, pdata, qn, vn)\n",
    "        Em_vals.append(e)\n",
    "\n",
    "    return states_, Em_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c04d73c6-f982-4445-acaf-4e118cfb8824",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the same q0, v0, u0 as above\n",
    "\n",
    "states_si, Em_vals_si = semi_implicit_integrator(q0, v0, u=u0, Tf=Tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f10c32f9-eafb-4aaf-b100-a7ceac793d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div style=\"height: 400px; width: 100%; overflow-x: auto; overflow-y: hidden; resize: both\">\n",
       "            <iframe src=\"http://127.0.0.1:7000/static/\" style=\"width: 100%; height: 100%; border: none\"></iframe>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vizer.display(q0)\n",
    "vizer.viewer.jupyter_cell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "48fe3e03-46ae-4c4c-9a5b-2e60381e3846",
   "metadata": {},
   "outputs": [],
   "source": [
    "states_si = np.stack(states_si).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d392128-695f-479b-b603-eff90a1370ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_traj_si = states_si[: model.nq]\n",
    "\n",
    "vizer.play(q_traj, timestep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fbe8b3da-744f-4e35-8025-4255ebaf2ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparison: mechanical energy conservation')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 386,
       "width": 532
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "plt.plot(times, Em_vals_si, label=\"semi-implicit\")\n",
    "plt.plot(times, Em_vals, label=\"variational\")\n",
    "plt.xlabel(\"Time $t$\")\n",
    "plt.ylabel(\"$E_m$\")\n",
    "plt.legend()\n",
    "plt.title(\"Comparison: mechanical energy conservation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa52fbab-4673-4c09-8b92-e2d254dda35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time $t$')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 387,
       "width": 512
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# other comparison: angle theta0\n",
    "plt.figure(dpi=100)\n",
    "plt.plot(times, q_traj_si[0, :-1], label=\"semi-implicit\")\n",
    "plt.plot(times, q_traj[0, :-1], label=\"variational\")\n",
    "plt.legend()\n",
    "plt.title(r\"Comparison: trajectory of angle $\\theta_0$\")\n",
    "plt.xlabel(\"Time $t$\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pin3]",
   "language": "python",
   "name": "conda-env-pin3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
